Reducing textured ir patterns in stereoscopic depth sensor imaging

ABSTRACT

Systems, devices, and techniques related to removing infrared texture patterns used for depth sensors are discussed. Such techniques may include applying a color correction transform to raw input image data including a residual infrared texture pattern to generate output image data such that the output image data has a reduced IR texture pattern residual with respect to the raw input image data.

BACKGROUND

In computer vision and other imaging and computing contexts, depthimages may be generated based on two (e.g., left and right or referenceand target) two-dimensional images of a scene. In particular, inassisted stereoscopic or active stereoscopic techniques, an infrared(IR) textured pattern is projected onto a scene such that the imagesobtained during exposure include the IR textured pattern as modified bythe scene. Such techniques may be advantageous when the scene itselfdoes not include a lot of texture (e.g., for blank white walls orsimilar scene elements). The obtained images including the IR textureare then used to generate a depth image using stereoscopic imagematching techniques or the like. Such depth image(s) may be used in awide variety of contexts.

Furthermore, it may be desirable to obtain a color image of the scenethat does not include the IR textured pattern for display to a user, foruse in computer vision, or for other purposes. Current techniques forobtaining a color image of the scene excluding the IR textured patternin addition to the image including the IR textured pattern are costly,power intensive, reduce available frame rates, and/or tend toundesirably increase the size of the imaging devices. For example,separate imagers may be used to obtain a color image without the IRtexture in addition to the image with the IR texture, which may addcost, power usage, and device size. In another example, timemultiplexing techniques may be used such that an IR projector is turnedon/off and separate images with IR texture (IR projector on) and withoutIR texture (IR projector off) images are obtained. However, suchtechniques limit frame rate and such implementations are susceptible toproblems caused by motion in the scene. Finally, image sensors areavailable that include a modified Bayer pattern of R, G, B, IRsub-pixels that may be used to extract RGB image data from IR imagedata. Alternatively, image sensors currently under development includean organic layer on the image sensor that may be activatedelectronically to selectively add IR or RGB sensitivity. However, suchimage sensors are undesirably large and expensive.

Therefore, current techniques do not provide for high quality imagesincluding and excluding the IR textured pattern that are cost effective,limit power usage and device size, and offer ease of implementation. Itis with respect to these and other considerations that the presentimprovements have been needed. Such improvements may become critical asthe desire to utilize depth images in a variety of applications becomesmore widespread.

BRIEF DESCRIPTION OF THE DRAWINGS

The material described herein is illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. For example, the dimensions of some elementsmay be exaggerated relative to other elements for clarity. Further,where considered appropriate, reference labels have been repeated amongthe figures to indicate corresponding or analogous elements. In thefigures:

FIG. 1 illustrates components of an example system for processing imagesto correct for an IR texture pattern residual and to generate depthmaps;

FIG. 2 illustrates an example device for processing images to correctfor an IR texture pattern residual and to generate depth maps;

FIG. 3 illustrates an example stereoscopic image matching;

FIG. 4 illustrates an example image sensor and an example color filterarray;

FIG. 5 illustrates a depiction of an example image with IR texture;

FIG. 6 illustrates a depiction of an example image corrected for IRtexture;

FIG. 7 illustrates an example system including a dual pipeline imagesignal processor;

FIG. 8 illustrates an example process for correcting for an IR texturepattern residual and generating depth maps;

FIG. 9 illustrates an example IR textured color image, an example IRcorrected color image and corresponding example depth images;

FIG. 10 is a flow diagram illustrating an example process for correctingfor an IR texture pattern residual in raw image data;

FIG. 11 is an illustrative diagram of an example system for correctingfor an IR texture pattern residual in raw image data;

FIG. 12 is an illustrative diagram of an example system; and

FIG. 13 illustrates an example small form factor device, all arranged inaccordance with at least some implementations of the present disclosure.

DETAILED DESCRIPTION

One or more embodiments or implementations are now described withreference to the enclosed figures. While specific configurations andarrangements are discussed, it should be understood that this is donefor illustrative purposes only. Persons skilled in the relevant art willrecognize that other configurations and arrangements may be employedwithout departing from the spirit and scope of the description. It willbe apparent to those skilled in the relevant art that techniques and/orarrangements described herein may also be employed in a variety of othersystems and applications other than what is described herein.

While the following description sets forth various implementations thatmay be manifested in architectures such as system-on-a-chip (SoC)architectures for example, implementation of the techniques and/orarrangements described herein are not restricted to particulararchitectures and/or computing systems and may be implemented by anyarchitecture and/or computing system for similar purposes. For instance,various architectures employing, for example, multiple integratedcircuit (IC) chips and/or packages, and/or various computing devicesand/or consumer electronic (CE) devices such as set top boxes, smartphones, etc., may implement the techniques and/or arrangements describedherein. Further, while the following description may set forth numerousspecific details such as logic implementations, types andinterrelationships of system components, logic partitioning/integrationchoices, etc., claimed subject matter may be practiced without suchspecific details. In other instances, some material such as, forexample, control structures and full software instruction sequences, maynot be shown in detail in order not to obscure the material disclosedherein.

The material disclosed herein may be implemented in hardware, firmware,software, or any combination thereof. The material disclosed herein mayalso be implemented as instructions stored on a machine-readable medium,which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

References in the specification to “one implementation”, “animplementation”, “an example implementation”, or such embodiments, orexamples, etc., indicate that the implementation, embodiment, or exampledescribed may include a particular feature, structure, orcharacteristic, but every implementation, embodiment, or example may notnecessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same implementation. Furthermore, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other implementations whether or not explicitlydescribed herein. The terms “substantially,” “close,” “approximately,”“near,” and “about,” generally refer to being within +/−10% of a targetvalue.

Methods, devices, apparatuses, computing platforms, and articles aredescribed herein related to image processing in depth sensors to removeinfrared (IR) texture pattern residuals from images or image data.

As described above, in some contexts, depth images may be generatedusing two (e.g., left and right or reference and target) two-dimensionalcolor images of a scene such that an infrared (IR) textured pattern hasbeen projected onto the scene during image capture. Such an IR texturedpattern provides IR texture pattern residuals in the captured image,which may improve stereoscopic image matching, particularly when thescene would not otherwise contain texture details for the matching. Insome embodiments discussed herein, the IR texture pattern residual maybe removed from the image or image data by applying a color correctiontransform to raw input image data including the IR texture patternresidual or image data corresponding to the raw input image dataincluding the IR texture pattern residual to generate output image datasuch that the color correction transform corrects for the IR texturepattern residual and the output image data has a reduced IR texturepattern residual with respect to the raw input image data and/or theimage data corresponding to the raw input image data.

The color correction transform may include any suitable color correctiontransform to correct for the IR texture pattern residual and the colorcorrection transform may be applied using any suitable technique ortechniques. In some embodiments, the color correction transform isapplied to raw image data from an image sensor having a color filterarray (CFA) thereon such that the color correction transform translatesfrom sub-pixel signals corresponding to colors of the CFA to pixelvalues of an output image (e.g., each pixel value including a red value,a green value, and a blue value). In other embodiments, the colorcorrection transform is applied to image data corresponding to the rawinput image data such that the raw input image data has been modified insome way such as smoothing, bias adjustments, or the like prior to theapplication of the color correction transform.

Such techniques provide for output images of a scene with little or noIR texture pattern residual as well as depth maps or depth imagescorresponding to the scene. Such output images may be presented to auser (e.g., without the unsightly IR texture pattern residual), used incomputer vision analysis or applications (e.g., without the IR texturepattern residual which may cause analysis failures) such as edgedetection, object detection, object tracking, gesture recognition anddevice control based on such gestures, facial pose recognition anddevice control based on such facial gestures, three-dimensional scenereconstruction, scene understanding, virtual reality, augmented reality,etc. Such depth maps or depth images may also be used in such computervision analysis or applications. Such techniques may offer the advantageof providing left and right color images without texture that areperfectly registered and maintain calibration with respect to the leftand right color images with texture. For example, systems that haveexternal color imagers have calibration difficulties such that everycolor pixel is not matched with resultant depth pixels. Such matching ofcolor image pixels (e.g., R, G, B pixels of color images withouttexture) and depth pixels (e.g., D pixels of resultant depth images) maybe critical in a variety of use cases such as background segmentation(e.g., green screening), object detection, object extraction, and thelike.

FIG. 1 illustrates components of an example system 100 for processingimages to correct for an IR texture pattern residual and to generatedepth maps, arranged in accordance with at least some implementations ofthe present disclosure. As shown in FIG. 1, system 100 may include animage signal processor (ISP) 101 to implement an IR correction colorconversion transform module 102 and an IR texture color conversiontransform module 103, a left camera 104, a right camera 105, an IRtransmitter 106, a stereo matching module 107, a memory 108, a display109, and a computer vision module 110. Also as shown, IR transmitter 106projects an IR texture pattern 116 onto a scene 121 such that an IRtexture pattern residual is obtained when an image or image data arecaptured corresponding to scene 121 by left camera 104 and right camera105. For example, infrared transmitter 106 illuminates scene 121 withinfrared light and left camera 104 and right camera 105 attain left (L)raw image 111 and right (R) raw image 112 based on scene 121 and theillumination of IR texture pattern 116 provided via IR transmitter 116.IR transmitter 106 may be any suitable IR transmitter such as an IRlaser or the like and IR texture pattern 116 may include any suitablepattern such as a grid pattern, or the like.

Left camera 104 and right camera 105 may include any suitable colorcamera or color camera modules each including, for example, an imagesensor and a color filter array covering the image sensor such that theimage sensor detects and conveys the data or information of an image byconverting light (as they pass through the color filter array) intosignals or signal values. As is discussed further herein, the colorfilter array has elements that allow a particular color (i.e., red,green, or blue) of light to pass through the element. However, suchcolor filter array elements are imperfect and allow other colors as wellas IR light to pass such that all light allowed to pass is detected bysub-pixels of the image sensors. As such, raw image data (e.g., left rawimage 111 and right raw image 112) from left camera 104 and right camera105 (or the image sensors thereof) includes a residual of the discussedIR texture pattern 116.

As discussed further herein, system 100 may correct, via a colorcorrection transform to remove IR, for the residual of IR texturepattern 116 in one or both of left raw image 111 and right raw image 112to generate one or more color images with IR correction 113.Furthermore, system 100 may generate, via another color correctiontransform to retain IR texture, left and right images with IR texture114. As shown, color image(s) with IR correction 113 may be stored tomemory 108 for eventual display to a user via display 109, for use incomputer vision via computer vision module 110, or for other uses whereit is desirable to have an image with the IR texture corrected orremoved. Furthermore, left and right images with IR texture 114 may beprovided for use by stereo matching module 107 to generate a depth map115 via stereo matching techniques.

System 100 or any combination of components thereof may be implementedvia any suitable device such as a depth sensor, a depth sensor module,or the like. Although discussed herein with respect to implementationvia a depth sensor module, system 100 may be implemented in any othersuitable imaging device such as a personal computer, a laptop computer,a tablet, a phablet, a smart phone, a digital camera, a gaming console,a wearable device, a set top device, or the like.

FIG. 2 illustrates an example device 200 for processing images tocorrect for an IR texture pattern residual and to generate depth maps,arranged in accordance with at least some implementations of the presentdisclosure. As shown in FIG. 2, device 200 may include left camera 104,right camera 105, IR transmitter 106, and a motherboard 201 toimplement, within a housing 202 of device 200, stereo matching module107, memory 108, ISP 101, and computer vision module 110. Also as shown,device 200 may include a display port 203 to transmit image data forpresentment to a user via display 109, which may be implemented as anintegrated component of device 200 or separately from device 200.

With reference to FIGS. 1 and 2, in some embodiments, stereo matchingmodule 107 is implemented via hardware (e.g., a graphics processor orISP 101) to generate depth map 115 or a depth image based on left andright images with IR texture 114. For example, left and right imageswith IR texture 114 may include red-green-blue (RGB) image data suchthat each pixel location of left and right images with IR texture 114includes a red value, a green value, and a blue value. Althoughdiscussed with respect to RGB image data, left and right images with IRtexture 114 and/or color images with IR correction 113 may be in anysuitable color space such as YUV, YCbCR, or the like. For example,stereo matching module 107 may generate a depth image or a depth mapsuch as depth map 115 based on a search of a target image (i.e., rightimage) based on a window generated around a pixel location in areference (i.e., left image) image. As shown, left camera 104 and rightcamera 105 may be horizontally aligned or substantially horizontallyaligned with respect to scene 121 to attain images (i.e., left and rightimages with IR texture 114) to perform stereoscopic image matching forscene 121 as is discussed further herein.

FIG. 3 illustrates an example stereoscopic image matching 300, arrangedin accordance with at least some implementations of the presentdisclosure. As shown in FIG. 3, stereoscopic image matching 300 mayinclude attaining left and right images with IR texture 114 a and 114 b,respectively, of scene 121, which may include an example surface 310. Asdiscussed, left and right images with IR texture 114 may include a leftor reference image 114 a and a right or target image 114 b attained viaa left camera 104 and a right camera 105 and image processing discussedfurther herein, respectively. As illustrated, in some examples, the leftimage may be the reference image and the right image may be the targetimage. In other examples, the right image may be the reference image andthe left image may be the target image and/or additional target imagesmay be used. Furthermore, scene 121 may include any suitable sceneincluding indoor or outdoor scenes, scenes including objects and/orpeople, and so on.

Stereo matching techniques may determine a depth image based ontriangulating correspondences. For example, as shown in FIG. 3, givenleft and right images with IR texture 114, each including arepresentation of three-dimensional point x on surface 310, the depth,d, of x, may be determined based on d=f*b/disp, where f and b are thefocal length and base line, respectively, and disp, is the disparity forx, indicating the pixel displacement of x between left and right imageswith IR texture 114 (e.g., x_(L)-x_(R), where x_(L), and x_(R) are theprojections of x onto left and right images with IR texture 114,respectively). To determine the disparity, a rectangular template orwindow may be formed around x_(L), on the left or reference image (e.g.,left image with IR texture 114 a) and search windows in the right ortarget image (e.g., right image with IR texture 114 b) may be searchedhorizontally for the best match. Such a process may be repeated for allor some pixels of left and right images with IR texture 114 to generatedisparity values for the associated pixel locations. Such disparityvalues may be translated to depth values (e.g., such that d=f*b/disp)and the resultant depth image or map (e.g., depth map 115) may includedepth values for such pixels. As discussed, the inclusion of theresidual of IR texture pattern 116 within left and right images with IRtexture 114 may aid in the discussed stereoscopic image matching.

Referring again to FIG. 1, during the illumination of IR texture pattern116 onto scene 121 by IR transmitter 106, left camera 104 and rightcamera 105 attain left raw image 111 and right raw image 112. Asdiscussed, it is advantageous to include an IR texture pattern residualin the images for stereoscopic image matching to increase the accuracyof the matching particularly in low detail or low texture scenes. Asshown in FIG. 1, left raw image 111 and right raw image 112 are providedto IR texture color conversion transform module 103. IR texture colorconversion transform module 103 receives left raw image 111 and rightraw image 112 and IR texture color conversion transform module 103generates left and right images with IR texture 114. IR texture colorconversion transform module 103 may generate left and right images withIR texture 114 using any suitable technique or techniques. In someembodiments, IR texture color conversion transform module 103 applies acolor correction transform or matrix to left raw image 111 and right rawimage 112 (e.g., image data from an image sensor) or to image datacorresponding to left raw image 111 and right raw image 112 to generateleft and right images with IR texture 114 such that left and rightimages with IR texture 114 retain residual IR texture.

Furthermore, it may be advantageous to provide one or more color imageswith IR correction 113 for presentment to a user, for use in computervision, or for other uses such that color image(s) with IR correction113 have corrected, reduced, or eliminated IR residual texture. Asshown, left raw image 111 and/or right raw image 112 are provided to IRcorrection color conversion transform module 102. IR correction colorconversion transform module 102 receives one or both of left raw image111 and right raw image 112 and IR correction color conversion transformmodule 102 generates one or more color images with IR correction 113. Inan embodiment, only one of left raw image 111 or right raw image 112 areused to generate a single color image with IR correction 113. In anembodiment, both left raw image 111 or right raw image 112 are used togenerate two color images with IR correction 113. IR correction colorconversion transform module 102 may generate color image(s) with IRcorrection 113 using any suitable technique or techniques. In someembodiments, IR correction color conversion transform module 102 appliesa color correction transform or matrix to left raw image 111 and/orright raw image 112 (e.g., image data from an image sensor) or to imagedata corresponding to left raw image 111 and right raw image 112 (e.g.,raw image data that has been preprocessed) to generate color image(s)with IR correction 113.

For example, left raw image 111 and right raw image 112 may include anysuitable raw images or raw image data or the like. In an embodiment,each of left camera 104 and right camera 105 include an image sensor(e.g., a complementary metal-oxide-semiconductor (CMOS) sensor) having ared-green-blue color filter array thereon and/or image pre-processingmodules or components to provide left raw image 111 and right raw image112.

FIG. 4 illustrates an example image sensor 401 and an example colorfilter array 402, arranged in accordance with at least someimplementations of the present disclosure. As shown in FIG. 4, colorfilter array 402 may include an array of color filter elements 403 suchthat each color filter element of color filter elements 403 such ascolor filter elements 404, 405, 406, 407 (with each color filter elementbeing red (R), green (G), or blue (B) in the illustrated embodiment) ofcolor filter array 402 attempts to block all other colors of light(e.g., R color filter element 404 attempts to block all but red light, Gcolor filter elements 405, 406 attempt to block all but green light, andB color filter element 407 attempts to block all but blue light).Furthermore, each of color filter elements 403 corresponds to anindividual sub-pixel (not shown) of image sensor 401. Image sensor 401,during image capture, generates a signal for each sub-pixel thereof,which may be provided as raw image 411. For example, raw image 411 mayinclude a sub-pixel signal for each sub-pixel of image sensor 401 suchthat, as discussed, each sub-pixel signal corresponds to an R, G, or Bcolor filter element of color filter elements 403.

Although each of color filter elements 403 attempts to block all otherlight except for the band of light corresponding thereto, color filterelements 403 invariably leak other colors of light into thecorresponding sub-pixel of image sensor 401. For example, bluesub-pixels (i.e., sub-pixels having a B color filter array element suchas color filter element 407) respond to green and red light as well asIR (i.e., about 850 nm) light. Similarly, red sub-pixels (i.e.,sub-pixels having a R color filter array element such as color filterelement 404) respond to green, blue, and IR light and green sub-pixels(i.e., sub-pixels having a G color filter array element such as colorfilter elements 405, 406) respond to red, blue, and IR light.

Furthermore, groupings of color filter elements and correspondingsub-pixels of image sensor 401 as illustrated with respect to grouping408 of color filter elements 404, 405, 406, 407 and the correspondingsub-pixels of image sensor 401 (not shown) may be used to generate asingle pixel value of an output image as is discussed further hereinbelow. For example, the signals or signal values corresponding to thesub-pixels of grouping 408 (i.e., a red signal value, Rs, of thesub-pixel corresponding to color filter element 404, a green signalvalue, Gs1, of the sub-pixel corresponding to color filter element 405,a green signal value, Gs2, of the sub-pixel corresponding to colorfilter element 406, and a blue signal value, Bs, of the sub-pixelcorresponding to color filter element 407) may be used to determine asingle pixel value (having a red value, R, a green value, G, and a bluevalue, B) for a pixel position of an output image based on a colorconversion transform or matrix as discussed below.

Color filter array 402 may include any suitable color filter arraypattern such as a Bayer color filter array pattern, a Yamanaka colorfilter array pattern, a Lukac color filter array pattern, a stripedcolor filter array pattern, or a diagonal striped color filter arraypattern. Furthermore, image sensor 401 may be any suitable image sensorsuch as a CMOS sensor. Image sensor 401 and color filter array 402 maybe implemented as a part of one or both of left camera 104 and rightcamera 105.

Returning to FIG. 1, ISP 101 is coupled to the image sensors of leftcamera 104 and right camera 105 and ISP 101 receives left raw image 111and right raw image 112 having characteristics as discussed with respectto raw image 411. ISP 101 may be any suitable image signal processorsuch as an application specific integrated circuits (ASIC), aprogrammable logic devices (PLD), or a digital signal processor (DSP).IR correction color conversion transform module 102 applies a colorcorrection transform or matrix directly to left raw image 111 and/orright raw image 112 or to corresponding image data (e.g., after somepreprocessing) to correct for the IR texture pattern residual providedby IR texture pattern 116 to remove or substantially remove the IRtexture pattern residual to generate or more color images with IRcorrection 113 such that one or more color images with IR correction113.

The color correction transform to provide IR correction (removal orreduction) may be any suitable color correction transform thattranslates from sub-pixel signals of a raw input image to pixel valuesof an output image. For example, the raw input image may includesub-pixel signals corresponding to R sub-pixels, G sub-pixels, and Bsub-pixels as discussed with respect to FIG. 4. The color correctiontransform to provide IR correction may be applied to multiple sub-pixelsignals of the raw input image or raw input image data to generate anindividual pixel of an output image having IR correction. Suchprocessing is repeated for any number of individual pixels of the outputimage. In an embodiment, the color correction transform provides aconvolution of the raw input image or raw input image data to generatethe output image having IR correction. In an embodiment, the colorcorrection transform may be applied to sub-pixel regions of the rawinput image or raw input image data as shown in Equation (1):

$\begin{matrix}{\begin{bmatrix}R_{C} \\G_{C} \\B_{C}\end{bmatrix} = {\begin{bmatrix}a_{11} & a_{12} & a_{13} & a_{14} \\a_{21} & a_{22} & a_{23} & a_{24} \\a_{31} & a_{32} & a_{33} & a_{34}\end{bmatrix}\begin{bmatrix}{Rs} \\{{Gs}\; 1} \\{{Gs}\; 2} \\{Bs}\end{bmatrix}}} & (1)\end{matrix}$

where a₁₁-a₃₄ are color correction transform or matrix coefficients, R,Gs1, Gs2, and Bs are sub-pixel signal values for a particular raw imageregion (i.e., sub-pixel signal values for grouping 408), and R_(C),G_(C), and B_(C) are pixel values for an individual pixel of an outputimage or of output image data having IR correction (C). For example, acolor image of one or more color images with IR correction 113 mayinclude an R_(C), G_(C), and B_(C) value for each pixel location thereofas determined using Equation (1). The color correction transform ormatrix may include or implement any suitable color correction transformor matrix coefficients that provide IR correction implemented using anysuitable technique or techniques. In an embodiment, the color correctiontransform or matrix coefficients are implemented via a look up table(LUT) accessible to IR correction color conversion transform module 102.

Similarly, in some embodiments, IR texture color conversion transformmodule 103 applies a color correction transform or matrix directly toleft raw image 111 and right raw image 112 such that IR texture colorconversion transform module 103 applies a color correction transform toleft raw image 111 and/or right raw image 112 to retain the IR texturepattern residual provided by IR texture pattern 116 for use instereoscopic matching. The color correction transform to retain IRcorrection may be any suitable color correction transform thattranslates from sub-pixel signals of a raw input image to pixel valuesof an output image as discussed above. For example, the color correctiontransform to retain IR correction may be applied to multiple sub-pixelsignals of the raw input image or raw input image data to generate anindividual pixel of an output image having IR correction as discussed.In an embodiment, the color correction transform provides a convolutionof the raw input image or raw input image data to generate the outputimage having IR correction. In an embodiment, the color correctiontransform may be applied to sub-pixel regions of the raw input image orraw input image data as shown in Equation (2):

$\begin{matrix}{\begin{bmatrix}R_{IR} \\G_{IR} \\B_{IR}\end{bmatrix} = {\begin{bmatrix}b_{11} & b_{12} & b_{13} & b_{14} \\b_{21} & b_{22} & b_{23} & b_{24} \\b_{31} & b_{32} & b_{33} & b_{34}\end{bmatrix}\begin{bmatrix}{Rs} \\{{Gs}\; 1} \\{{Gs}\; 2} \\{Bs}\end{bmatrix}}} & (2)\end{matrix}$

where b₁₁-b₃₄ are color correction transform or matrix coefficients, R,Gs1, Gs2, and Bs are sub-pixel signal values for a particular raw imageregion (i.e., sub-pixel signal values for grouping 408), and R_(IR),G_(IR), and B_(IR) are pixel values for an individual pixel of an outputimage or of output image data having IR texture (IR). For example, colorimages of left and right images with IR texture 114 may include anR_(IR), G_(IR), and B_(IR) value for each pixel location thereof asdetermined using Equation (2). The color correction transform or matrixmay include or implement any suitable color correction transform ormatrix coefficients that provide IR correction implemented using anysuitable technique or techniques. In an embodiment, the color correctiontransform or matrix coefficients are implemented via a look up table(LUT) accessible to IR texture color conversion transform module 103.

In some embodiments, the resultant IR corrected output image(s) may beprovided as one or more color images with IR correction 113 and theresultant IR texture output image(s) may be provided as left and rightimages with IR texture 114. In other embodiments, one or both of colorimage(s) with IR correction 113 and/or left and right images with IRtexture 114 may be further processed along separate imaging pipelines asis discussed further herein with respect to FIG. 7.

As discussed, in some embodiments, IR correction color conversiontransform module 102 and/or IR texture color conversion transform module103 apply a color correction transform or matrix directly to left rawimage 111 and/or right raw image 112. In other embodiments, IRcorrection color conversion transform module 102 and/or IR texture colorconversion transform module 103 apply a color correction transform ormatrix to image data corresponding to left raw image 111 and/or rightraw image 112 such that the corresponding image data has beenpreprocessed to provide smoothing, remove outlier signals, etc. Suchpreprocessing may be performed by an image preprocessor within left andright cameras 104, 105 or by an image preprocessor or ISP 101.

FIG. 5 illustrates a depiction of an example image with IR texture 500,arranged in accordance with at least some implementations of the presentdisclosure. For example, image with IR texture 500 may be or may be apresentment of any IR texture output image data (e.g., output image datahaving retained IR texture) discussed herein such as left and rightimages with IR texture 114 generated based on IR texture colorconversion transform module 103 applying a color correction transform ormatrix on left raw image 111 and right raw image 112 to retain IRtexture. As shown in FIG. 5, image with IR texture 500 includes an IRtexture pattern residual 501 (i.e., white dots in the illustration) fromIR texture pattern 116 being projected on scene 121. As shown, in anembodiment, IR texture pattern residual 501 may have a grid like patternof IR dots or specks or the like. However, IR texture pattern residual501 may have any suitable pattern shape such as a random specklepattern, a concentric ring pattern, or the like of any suitable IRprojected shapes such as squares, rectangles, diamonds, or the like.Scene 121 may include any suitable scene. In the illustrated embodiment,scene 121 includes a foreground object 503 (e.g., a table) and abackground 502.

As discussed, in some scenes, particularly those without native texture,IR texture pattern residual 501 may improve stereoscopic matching withanother image with an IR texture residual from a matching IR texturepattern 116 being projected onto the scene. Furthermore, as shown withrespect to pixel position 504, IR texture pattern residual 501 may beprovided at a plurality of pixel positions within image with IR texture500 such that the pixel positions of IR texture pattern residual 501tend to have a greater luminance with respect to other pixel positionsof image with IR texture 500. Although this will not be true of everynon-IR texture pattern residual pixel position (i.e., some non-IRtexture pattern residual pixel positions may also have high luminancewithin a scene), such greater luminance at pixel positions of IR texturepattern residual 501 will tend to occur due to the projection of IRtexture pattern 116 and the sensing of the pattern by, for example,image sensor 401.

FIG. 6 illustrates a depiction of an example image corrected for IRtexture 600, arranged in accordance with at least some implementationsof the present disclosure. For example, image corrected for IR texture600 may be or may be a presentment of any output image or output imagedata corrected for an IR residual texture pattern by application of acolor correction transform or matrix as discussed herein. For example,image corrected for IR texture 600 may be one of one or more colorimages with IR correction 113 generated based on IR correction colorconversion transform module 102 applying a color correction transform ormatrix on left raw image 111 or right raw image 112 to correct for orremove IR texture. As shown in FIG. 6, image corrected for IR texture600 includes foreground object 503 and background 502 presented withoutIR texture pattern residual 501. As discussed, such a presentment may beadvantageous for presentment to a user, for use in computer vision,image analysis, and other applications.

Furthermore, as shown with respect to pixel position 504, which is thesame pixel position as pixel position 504, the high luminance of pixelposition 504 within image with IR texture 500 is removed at pixelposition 504 of image corrected for IR texture 600. For example, withreference to FIGS. 5 and 6, as is clear from a visual evaluation ofimage with IR texture 500 and image corrected for IR texture 600, imagecorrected for IR texture 600 has a reduced IR texture pattern residualwith respect to image with IR texture 500 (and the corresponding rawinput image data used to generate image with IR texture 500). Forexample, the conclusion that image corrected for IR texture 600 has areduced IR texture pattern residual with respect to image with IRtexture 500 may determined using any suitable image evaluationtechniques such as a visual evaluation, a pixel comparison at pixelpositions of IR texture pattern residual 501 such as pixel position 504,or the like. In an embodiment, image with IR texture 500 includes IRtexture pattern residual 501 at a plurality of pixel positions withinimage with IR texture 500 including pixel position 504. IR texturepattern residual 501 may be at any number of pixel positions withinimage with IR texture 500 such as hundreds, thousands, tens ofthousands, or more pixel positions. In an embodiment, each IR textureelement (e.g., dot) may be used to define a corresponding pixel positionof image with IR texture 500. In other embodiments, each IR textureelement (e.g., dot) may be used to define a plurality of correspondingpixel position of image with IR texture 500. Furthermore, in thefollowing discussion each and every IR texture element may be used oronly a subset thereof may be used.

In an embodiment, image corrected for IR texture 600 having a reduced IRtexture pattern residual with respect to image with IR texture 500includes image with IR texture 500 having an average luminance at theplurality of pixel positions within image with IR texture 500 that haveIR texture pattern residual 501 such as pixel position 504 and imagecorrected for IR texture 600 having an average luminance at the sameplurality of pixel positions within image corrected for IR texture 600that is less than the average luminance at the plurality of pixelpositions within image with IR texture 500. The average luminance may bedetermined using any suitable technique or techniques. For example, theluminance at a particular pixel position may be determined from R, G, Bpixel values as k₁*R+k₂*G+k₃*B where k₁, k₂, k₃ are conversionsconstants such as k₁˜0.2-0.3, k₂˜0.5-0.6, k₃˜0.07-0.2, or the like. Inother examples, the luminance at a particular pixel position may bedetermined as k₁*R̂2+k₂*Ĝ2+k₃*B̂2. Furthermore, although discussed withrespect averaging the pixel position luminance values, other techniquesmay be used determining a median of the pixel position luminance values.

As discussed, the correction, reduction, or elimination of IR texturepattern residual 501 from raw image data to generate image corrected forIR texture 600 and the retention of IR texture pattern residual 501 fromraw image data to generate image with IR texture 500 may be attained byapplying different color correction transforms or matrices to the rawimage data (i.e., left (L) raw image 111 and right (R) raw image 112).The color correction transforms or matrices may include any suitablecolor correction transforms or matrices that may be tuned or designed orthe like relative to the image sensor from which the raw image data wasattained. In an embodiment, the color correction transforms or matriceshave at least one different color correction transform or matrixcoefficients. In an embodiment, the color correction transforms ormatrices have different color correction transform or matrixcoefficients such that all of the color correction transform or matrixcoefficients are different. For example, any number of color correctiontransform or matrix coefficients, a₁₁-a₃₄, for correcting for IR textureresiduals may be different with respect to any number of colorcorrection transform or matrix coefficients, b₁₁-b₃₄, for retaining forIR texture residuals.

In some embodiments, the color images with IR correction (e.g., one ormore color images with IR correction 113) may be saved to memory foreventual presentment to a user for eventual use in computer visionapplications or the like. In other embodiments, the color images with IRcorrection (e.g., one or more color images with IR correction 113) maybe further processed by an imaging pipeline prior to being saved tomemory. Similarly, in some embodiments, the color images with IR texture(e.g., color images of left and right images with IR texture 114) may besaved to memory or provided to a stereo matching module or component foreventual use in the generation of a depth map or image and/or for use incomputer vision applications or the like. In other embodiments, thecolor images with IR texture (e.g., color images of left and rightimages with IR texture 114) may be further processed by a separateimaging pipeline.

FIG. 7 illustrates an example system 700 including a dual pipeline imagesignal processor, arranged in accordance with at least someimplementations of the present disclosure. As shown in FIG. 7, system700 may include image sensor 401 and color filter array 402 as discussedherein with respect to FIG. 4. For example, image sensor 401 and colorfilter array 402 may be implemented by one or both of left camera 104and right camera 105. As shown, image sensor 401 generates raw imagedata 705 based on an image capture of a scene having an IR textureprojection such that raw image data 705 includes an IR texture patternresidual as discussed herein. Also, as shown, system 700 includes animage signal processor (ISP) 701 implementing an IR correction pipeline711 and an IR texture pipeline 721. In an embodiment, ISP 701 may beimplemented as ISP 101 in any system or device discussed herein.

IR correction pipeline 711 includes an IR correction color correctiontransform (CCT) module 712 to implement a color correction transform tocorrect, reduce, or eliminate an IR residual texture pattern asdiscussed herein. IR correction CCT module 712 may apply the IRcorrection color correction transform directly to raw image data 705 orto image data corresponding to raw image data 705. For example, acomponent or module of ISP 701 within IR correction pipeline 711 andbefore IR correction CCT module 712 or prior to both IR correctionpipeline 711 and IR texture pipeline 721 may apply smoothing, outlierremoval, or other operations to preprocess raw image data 705 prior toprocessing by IR correction CCT module 712. In any event, IR correctionCCT module 712 applies a color correction transform or matrix to rawimage data 705 or to image data corresponding to raw image data 705 togenerate color image data corresponding thereto having the IR texturepattern residual removed. For example, raw image data 705 or image datacorresponding to raw image data 705 may be in a sub-pixel signal valuespace and the color correction transform or matrix may translate rawimage data 705 or image data corresponding to raw image data 705 into apixel space such that each pixel location of the output image data has aplurality of color channel values (e.g., R-G-B values) as discussedelsewhere herein such as with respect to Equation (1).

Furthermore, after such conversion to remove the IR texture patternresidual, IR correction pipeline 711 may include any suitable imageprocessing stages, components or modules as illustrated with respect toauto level module 713, white balance module 714, and noise filter 715.For example, auto level module 713 may provide linear adjustments ofpixel intensities for improved contrast, white balance module 714 mayprovide global adjustment of the intensities of the colors to accuratelyrepresent neutral colors, and noise filter 715 to reduce noise in theimage. In addition or in the alternative, IR correction pipeline 711 mayinclude a gamma correction module, a tone correction module, or otherprocessing modules. Such modules may be implemented using any suitabletechnique or techniques. As shown, after such processing, IR correctionpipeline 711 provides an IR corrected color image 716, which may beprovided as one or more color images with IR correction 113 as discussedherein.

Also as shown in FIG. 7, IR texture pipeline 721 includes a colorcorrection transform module 722 to implement a color correctiontransform to retain an IR residual texture pattern as discussed herein.Color correction transform module 722 may apply the IR correction colorcorrection transform directly to raw image data 705 or to image datacorresponding to raw image data 705. For example, a component or moduleof ISP 701 within IR texture pipeline 721 and before color correctiontransform module 722 or prior to both IR correction pipeline 711 and IRtexture pipeline 721 may apply smoothing, outlier removal, or otheroperations to preprocess raw image data 705 prior to processing by colorcorrection transform module 722. Color correction transform module 722applies a color correction transform or matrix to raw image data 705 orto image data corresponding to raw image data 705 to generate colorimage data corresponding thereto that retains an IR texture pattern asdiscussed herein. For example, raw image data 705 or image datacorresponding to raw image data 705 may be in a sub-pixel signal valuespace and the color correction transform or matrix may translate rawimage data 705 or image data corresponding to raw image data 705 into apixel space such that each pixel location of the output image data has aplurality of color channel values (e.g., R-G-B values) as discussedelsewhere herein such as with respect to Equation (2).

Furthermore, after such color conversion, IR texture pipeline 721 mayinclude any suitable image processing stages, components or modules asillustrated with respect to auto level module 723, white balance module724, and noise filter 725, which may provide processing as discussedwith respect to IR correction pipeline 711. In addition or in thealternative, IR texture pipeline 721 may include a gamma correctionmodule, a tone correction module, or other processing modules. Suchmodules may be implemented using any suitable technique or techniques.As shown, after such processing, IR texture pipeline 721 provides an IRtexture color image 726, which may be provided as color images of leftand right images with IR texture 114 as discussed herein.

As shown, in an embodiment, ISP 701 includes IR correction pipeline 711and IR texture pipeline 721. In such an embodiment, IR correctionpipeline 711 may process one or both of left and right images (e.g.,system 700 may include another image sensor and color filter arrayanalogous to image sensor 401 and color filter array 402 such that leftand right images of a scene are obtained as discussed herein) and IRtexture pipeline 721 may process both the left and right images. Inanother embodiment, ISP 701 includes a second IR texture pipeline and/ora second IR correction pipeline such that the left and right images maybe processed at least partially in parallel.

FIG. 8 illustrates an example process 800 for correcting for an IRtexture pattern residual and generating depth maps, arranged inaccordance with at least some implementations of the present disclosure.Process 800 may include one or more operations 801-808 as illustrated inFIG. 8. Process 800 or portions thereof may be performed by any deviceor system discussed herein to generate depth maps or images using colorimages having an IR texture pattern and to remove an IR texture patternresidual from raw image data to generate a color image with the IRtexture pattern corrected or removed. Process 800 or portions thereofmay be repeated for any number of raw input images, or the like.

Process 800 begins at operation 801, where an IR pattern is projectedonto a scene. The IR pattern may be projected onto any scene using anysuitable technique or techniques. In an embodiment, an IR projectorprojects a predetermined IR pattern onto a scene. Processing continuesat operation 802, where an image capture is performed to obtain left andright raw input image data of the scene during projection of the IRpattern. As discussed, the obtain left and right raw input image datamay be attained by left and right cameras each having an image sensorand a corresponding color filter array such that the left and rightcameras are aligned horizontally for stereo matching. As discussed, dueto imperfections of the color filter array, the raw input image data maybe include an IR texture pattern residual corresponding to theprojection of the IR pattern.

Processing continues at decision operation 803, where a determinationmay be made as to whether IR texture pattern removal is enabled. Forexample, IR texture pattern removal may be enabled or disabled by auser, by system settings, or the like based on whether color images withremoved IR texture patterns are needed for presentment to a user, foruse in computer vision, etc. or not. If IR texture pattern removal isdisabled, processing continues at operation 806 as discussed below.

If IR texture pattern removal is enabled, processing continues atoperation 804, where one or both of the left raw input image data andthe right raw input image data are processed to correct for the IRtexture pattern residual as discussed herein to generate left and/orright color images corrected for the IR texture pattern residual. Forexample, the raw input image data or image data corresponding to the rawinput image data may be processed via application of a color correctiontransform or matrix as discussed herein that has been tuned or designedto remove the IR signal from the raw input image data or image datacorresponding to the raw input image data. In an embodiment, the colorcorrection transform or matrix transforms raw input image data from asub-pixel domain including sub-pixel signals corresponding to colorfilter array elements to a pixel domain such that each pixel locationincludes multiple color channel values such as R-G-B color channelvalues.

Processing continues at operation 805, where the resultant colorimage(s) corrected for the IR texture pattern residual may be stored inmemory for eventual presentment to a user (e.g., via a display device)and/or for further processing such as computer vision processing or thelike. The color image(s) corrected for the IR texture pattern residualmay be stored in any suitable format such as an image format having an Rvalue, a G value, and a B value for each pixel location thereof.

Processing continues from operation 805 or decision operation 803 atoperation 806, where the left raw input image data and the right rawinput image data are processed to generate left and right images havingIR texture (e.g., left and right IR texture images). For example, theraw input image data or image data corresponding to the raw input imagedata may be processed via application of a color correction transform ormatrix as discussed herein that has been tuned or designed to retain theIR signal from the raw input image data or image data corresponding tothe raw input image data. In an embodiment, as discussed with respect tooperation 805, the color correction transform or matrix transforms rawinput image data from a sub-pixel domain including sub-pixel signalscorresponding to color filter array elements to a pixel domain such thateach pixel location includes multiple color channel values such as R-G-Bcolor channel values.

Processing continues at operation 807, where a depth map or image isgenerated based on the left and right texture images generated atoperation 806. The depth map or image may be generated using anysuitable technique or techniques such as stereoscopic search techniques.Processing continues at operation 808, where the resultant depth map orimage may be stored in memory for further processing such as computervision processing or the like. The depth map or image may be stored inany suitable format such as an image format having a depth or disparityvalue for each pixel location of the depth map or image.

As discussed, an IR texture pattern residual may be removed from inputimage data to generate an output image having the IR texture patternresidual corrected, reduced, or eliminated. For example, tuning a colorcorrection transform or matrix may include changing the weights of thesub-pixel responses to optimize an image sensor such as a CMOS sensor toperform demosaicing of the pattern provided by a color filter array suchas an R-G-G-B Bayer pattern. As discussed, blue sub-pixels will alsorespond to green and red light as well as IR light (e.g., 850 nm light.Similarly, red sub-pixels will respond to green and blue light as wellas IR light and green sub-pixels will respond to blue and red light aswell as IR light. Such responses to IR light will be different for eachsub-pixel color (and dependent upon the sensor being implemented). Bytuning color correction transform or matrix (e.g., choosing colorcorrection coefficients) the IR signal is negated or minimized in the IRcorrected color images. Similarly, by tuning another color correctiontransform or matrix (e.g., choosing color correction coefficients), theIR signal is retained (and the discussed cross sub-pixel contaminationsmay be reduced or eliminated) in the IR textured color images (e.g.,those images that retain an IR correction residual).

The discussed, systems, devices, techniques, and articles provide IRtextured color images for stereoscopic image matching and IR correctedcolor images for display or use in computer vision or the like with theadvantages of reduced cost (e.g., no additional IR sensor or RGB-IRsensor or the like is needed), with increased frame rates (e.g., nomultiplexing of images taken with IR texture and images taken without IRtexture is needed), and with alignment in time of the depth images theIR corrected color images (e.g., the IR corrected color images and theIR textured color images may be processed substantially simultaneously).

FIG. 9 illustrates an example IR textured color image 901, an example IRcorrected color image 903 and corresponding example depth images 902,904, arranged in accordance with at least some implementations of thepresent disclosure. As shown in FIG. 9, IR textured color image 901includes an IR texture residual pattern, which is most evident on theshirt and face of the subject. For example, IR textured color image 901may be a left or right IR textured color image as discussed herein.Stereoscopic image matching may be performed using IR textured colorimage 901 and a corresponding IR textured color image (e.g., a rightimage if textured color image 901 is a left image or vice versa; notshown) to generate depth image 903. For example, depth image 903 mayhave excellent quality due, at least in part, to the IR texture residualpattern of textured color image 901 and the corresponding IR texturedcolor image.

Also as shown in FIG. 9, in IR corrected color image 903, the IR textureresidual pattern has been corrected, reduced, or eliminated, which isevident upon comparison to IR textured color image 901. For example, IRcorrected color image 903 and IR textured color image 901 are generatedbased on similar raw input image data via the application of differingcolor conversion transforms as discussed herein. Stereoscopic imagematching may be performed using an IR textured color image correspondingto IR corrected color image 903 (not shown) and another IR texturedcolor image (e.g., a right image if the IR textured color imagecorresponding to IR corrected color image 903 is a left image or viceversa; not shown) to generate depth image 904. For example, since depthimage 904 is based on IR textured color images, depth image 904 retainsexcellent results. Furthermore, IR corrected color image 903 is attainedfor presentment (e.g., such that the unsightly IR texture residualpattern is removed) or for use in computer vision, etc. (e.g., where theIR texture residual pattern may cause processing failures).

FIG. 10 is a flow diagram illustrating an example process 1000 forcorrecting for an IR texture pattern residual in raw image data,arranged in accordance with at least some implementations of the presentdisclosure. Process 1000 may include one or more operations 1001-1003 asillustrated in FIG. 10. Process 1000 may form at least part of an IRtexture pattern residual removal process. By way of non-limitingexample, process 1000 may form at least part of an IR texture patternresidual removal process as performed by any device, system, orcombination thereof as discussed herein. Furthermore, process 1000 willbe described herein with reference to system 1100 of FIG. 11, which mayperform one or more operations of process 1000.

FIG. 11 is an illustrative diagram of an example system 1100 forcorrecting for an IR texture pattern residual in raw image data,arranged in accordance with at least some implementations of the presentdisclosure. As shown in FIG. 11, system 1100 may include a centralprocessor 1101, a graphics processor 1102, a memory 1103, color cameras104, 105, IR transmitter 204, ISP 101 and/or display 109. Also as shown,central processor 1101 may include or implement stereo matching module107 and computer vision module 110 and ISP 101 may include or implementIR correction color conversion transform module 102 and IR texture colorconversion transform module 103. In the example of system 1100, memory1103 may store raw image data, color images, color image data, depthimages, depth image data, image data, and/or any other data as discussedherein.

As shown, in some embodiments, stereo matching module 107 and computervision module 110 are implemented by central processor 1101 and IRcorrection color conversion transform module 102 and IR texture colorconversion transform module 103 are implemented by ISP 101. In someembodiments, one or both of stereo matching module 107 and computervision module 110 are implemented by ISP 101 or graphics processor 1102.In some embodiments, one or both of IR correction color conversiontransform module 102 and IR texture color conversion transform module103 are implemented by central processor 1101 or graphics processor1102.

Graphics processor 1102 may include any number and type of graphicsprocessing units that may provide the discussed color conversion, stereomatching, and computer vision operations and/or other operations asdiscussed herein. For example, graphics processor 1102 may includecircuitry dedicated to manipulate image data, or the like obtained frommemory 1103. ISP 101 may include any number and type of image signal orimage processing units that may provide the discussed color conversion,stereo matching, and computer vision operations and/or other operationsas discussed herein. For example, ISP 101 may include circuitrydedicated to manipulate image data such as an ASIC or the like. Centralprocessor 1101 may include any number and type of processing units ormodules that may provide control and other high level functions forsystem 1100 and/or provide the discussed color conversion, stereomatching, and computer vision operations and/or other operations asdiscussed herein. Memory 1103 may be any type of memory such as volatilememory (e.g., Static Random Access Memory (SRAM), Dynamic Random AccessMemory (DRAM), etc.) or non-volatile memory (e.g., flash memory, etc.),and so forth. In a non-limiting example, memory 1103 may be implementedby cache memory.

In an embodiment, one or more or portions of stereo matching module 107,computer vision module 110, IR correction color conversion transformmodule 102, and IR texture color conversion transform module 103 may beimplemented via an execution unit (EU) of ISP 101 or graphics processor1102. The EU may include, for example, programmable logic or circuitrysuch as a logic core or cores that may provide a wide array ofprogrammable logic functions. In an embodiment, one or more or portionsof stereo matching module 107, computer vision module 110, IR correctioncolor conversion transform module 102, and IR texture color conversiontransform module 103 may be implemented via dedicated hardware such asfixed function circuitry or the like of ISP 101 or graphics processor1102. Fixed function circuitry may include dedicated logic or circuitryand may provide a set of fixed function entry points that may map to thededicated logic for a fixed purpose or function.

As discussed herein, cameras 104, 105 may attain raw image data of ascene including an IR texture pattern residual from IR light emitted byinfrared transmitter 204 to illuminate the scene. In an embodiment, oneor both of cameras 104, 105 include a CMOS sensor having ared-green-blue filter array thereon. Display 109 may display colorimages corrected for the IR texture pattern, depth images, or othergraphical interface information (e.g., responses based on gesture orface recognition or the like) generated based on such images and/r rawimage data.

Returning to discussion of FIG. 10, process 1000 may begin at operation1001, where raw input image data including an IR texture patternresidual from an IR texture pattern projected on a scene during an imagecapture of the scene may be received. For example, IR transmitter 204may project the IR texture pattern onto a scene and one or both ofcameras 104, 105 may generate raw input image data including the IRtexture pattern residual. In an embodiment, an image sensor of one orboth of cameras 104, 105 generates the raw input image data. In anembodiment, the raw input image data includes an IR texture patternresidual from the IR texture pattern. The raw input image data may bereceived from one of cameras 104, 105 by IR correction color conversiontransform module 102 as implemented by ISP 101.

Processing continues at operation 1002, where a color correctiontransform is applied to the raw input image data or image datacorresponding to the raw input image data to generate output image datasuch that the color correction transform is to correct for the IRtexture pattern residual to provide the output image data having areduced IR texture pattern residual with respect to the raw input imagedata. The color correction transform may be applied using any suitabletechnique or techniques. In an embodiment, applying the color correctiontransform includes applying the color correction transform directly tothe raw input image data. As used herein, the term raw input image dataindicates data from an image sensor or an image preprocessor of theimage sensor. In an embodiment, applying the color correction transformincludes applying the color correction transform to image datacorresponding to the raw input image data such that the image data is inthe same format as the raw input image data but has been preprocessed insome way. In an embodiment, the color correction transform is applied byIR correction color conversion transform module 102 as implemented byISP 101.

In an embodiment, applying the color correction transform includesapplying the color correction transform to a plurality of sub-pixelsignals of the raw input image data or image data corresponding to theraw input image data to generate a corresponding single pixel of theoutput image data such that the sub-pixel signals include at least onered sub-pixel signal value, at least one green sub-pixel signal value,and at least one blue sub-pixel signal value. In an embodiment, theplurality of sub-pixel signals consists of a single red sub-pixel signalvalue, two green sub-pixel signal values, and a single blue sub-pixelsignal value and the single pixel of the output image data comprises ared pixel value, a green pixel value, and a blue pixel value. Forexample, application of the color correction transform may provide atransform from a sub-pixel space (including regions of red, green,green, blue signals) to a pixel space (including a red, green, and bluevalue) for each pixel.

In an embodiment, the raw input image data processed at operation 1002corresponds to left or right raw input image data (e.g., from a left orright camera) for stereoscopic image matching. In an embodiment, secondraw input image data corresponding to the other of the left or rightcamera may be received and the color correction transform (e.g., thesame color correction transform) may be applied to the second raw inputimage data or image data corresponding to the second raw input imagedata to generate second output image data that is also corrected for IRtexture pattern residual. In such embodiments, color images correctedfor the IR texture pattern residual corresponding to both the left andright cameras (or image sensors) may be available for presentment to auser or for use in computer vision.

Processing continues at operation 1003, where the output image data isprovided for display to a user or for use in computer vision processingor the like. In an embodiment, the output image data is stored to memory1103. In an embodiment, the output image data is provided to computervision module 110 for further processing. In an embodiment, the outputimage data is provided to display 109 for presentment to a user.

As discussed, process 1000 provides for correcting for an IR texturepattern residual in raw image data to generate output image data suchthat the output image data has a reduced IR texture pattern residualwith respect to the raw image data. In an embodiment, the reduced IRtexture pattern residual is no IR texture pattern residual or anundetectable reduced IR texture pattern residual. As used herein theterm reduced IR texture pattern residual is meant to include the IRtexture pattern residual has been eliminated.

In an embodiment, process 1000 further includes applying a second colorcorrection transform (different with respect to the color correctiontransform implemented at operation 1002) to the raw input image data orthe image data corresponding to the raw input image data to generate IRtexture output image data. For example, the second color correctiontransform is to retain the IR texture pattern residual within the IRtexture output image data and the output image data has a reduced IRtexture pattern residual with respect to the IR texture output imagedata. In an embodiment, a plurality of pixel positions within the IRtexture output image data include the IR texture pattern residual andthe output image data having a reduced IR texture pattern residual withrespect to the IR texture output image data is indicated by the IRtexture output image data having a first average luminance at theplurality of pixel positions within the IR texture output image data andthe output image data having a second average luminance at the sameplurality of pixel positions within the output image data that is lessthan the first average luminance.

In an embodiment, the second color correction transform and the colorcorrection transform are implemented in different image processingpipelines of ISP 101. For example, the color correction transform may beimplemented in a first image processing pipeline of ISP 101 and thesecond color correction transform may be implemented in a second imageprocessing pipeline of ISP 101.

Furthermore, as discussed with respect to operation 1002, the raw inputimage data processed at operation 1002 may correspond to left or rightraw input image data (e.g., from a left or right camera) forstereoscopic image matching and second raw input image datacorresponding to the other of the left or right camera may be generatedand/or received. In an embodiment, the raw input image data is generatedby an image sensor and the second raw input image data is generated by asecond image sensor based on a second image capture of the scene havingthe projection of the IR texture pattern. For example, the second rawinput image data includes a second IR texture pattern residual from theIR texture pattern projected onto the scene. In an embodiment, the firstand second image sensors are horizontally aligned with respect to thescene to provide for stereo matching. In an embodiment, a depth map maybe generated based on the IR texture output image data corresponding tothe raw input image data (as discussed above) and second IR textureoutput image data corresponding to the second raw input image data. Forexample, the second IR texture output image data may be generated byapplying the second color correction transform to the second raw inputimage data or image data corresponding thereto. In an embodiment,generating the depth map includes performing stereoscopic image matchingbased on the IR texture image and the second IR texture image.

Process 1000 may be repeated any number of times either in series or inparallel for any number raw input images or the like. For example,process 1000 may provide for correcting for an IR texture patternresidual and generating depth maps for any number of image captureinstances during imaging or video capture or the like.

Various components of the systems described herein may be implemented insoftware, firmware, and/or hardware and/or any combination thereof. Forexample, various components of the systems discussed herein may beprovided, at least in part, by hardware of a computing System-on-a-Chip(SoC) such as may be found in a computing system such as, for example, asmartphone. Those skilled in the art may recognize that systemsdescribed herein may include additional components that have not beendepicted in the corresponding figures. For example, the systemsdiscussed herein may include additional components such ascommunications modules and the like that have not been depicted in theinterest of clarity.

While implementation of the example processes discussed herein mayinclude the undertaking of all operations shown in the orderillustrated, the present disclosure is not limited in this regard and,in various examples, implementation of the example processes herein mayinclude only a subset of the operations shown, operations performed in adifferent order than illustrated, or additional operations.

In addition, any one or more of the operations discussed herein may beundertaken in response to instructions provided by one or more computerprogram products. Such program products may include signal bearing mediaproviding instructions that, when executed by, for example, a processor,may provide the functionality described herein. The computer programproducts may be provided in any form of one or more machine-readablemedia. Thus, for example, a processor including one or more graphicsprocessing unit(s) or processor core(s) may undertake one or more of theblocks of the example processes herein in response to program codeand/or instructions or instruction sets conveyed to the processor by oneor more machine-readable media. In general, a machine-readable mediummay convey software in the form of program code and/or instructions orinstruction sets that may cause any of the devices and/or systemsdescribed herein to implement at least portions of the systems discussedherein or any other module or component as discussed herein.

As used in any implementation described herein, the term “module” or“component” refers to any combination of software logic, firmware logic,hardware logic, and/or circuitry configured to provide the functionalitydescribed herein. The software may be embodied as a software package,code and/or instruction set or instructions, and “hardware”, as used inany implementation described herein, may include, for example, singly orin any combination, hardwired circuitry, programmable circuitry, statemachine circuitry, fixed function circuitry, execution unit circuitry,and/or firmware that stores instructions executed by programmablecircuitry. The modules may, collectively or individually, be embodied ascircuitry that forms part of a larger system, for example, an integratedcircuit (IC), system on-chip (SoC), and so forth.

FIG. 12 is an illustrative diagram of an example system 1200, arrangedin accordance with at least some implementations of the presentdisclosure. In various implementations, system 1200 may be a mobilesystem although system 1200 is not limited to this context. System 1200may implement and/or perform any modules or techniques discussed herein.For example, system 1200 may be incorporated into a personal computer(PC), sever, laptop computer, ultra-laptop computer, tablet, touch pad,portable computer, handheld computer, palmtop computer, personal digitalassistant (PDA), cellular telephone, combination cellular telephone/PDA,television, smart device (e.g., smartphone, smart tablet or smarttelevision), mobile internet device (MID), messaging device, datacommunication device, cameras (e.g. point-and-shoot cameras, super-zoomcameras, digital single-lens reflex (DSLR) cameras), and so forth. Insome examples, system 1200 may be implemented via a cloud computingenvironment.

In various implementations, system 1200 includes a platform 1202 coupledto a display 1220. Platform 1202 may receive content from a contentdevice such as content services device(s) 1230 or content deliverydevice(s) 1240 or other similar content sources. A navigation controller1250 including one or more navigation features may be used to interactwith, for example, platform 1202 and/or display 1220. Each of thesecomponents is described in greater detail below.

In various implementations, platform 1202 may include any combination ofa chipset 1205, processor 1210, memory 1212, antenna 1213, storage 1214,graphics subsystem 1215, applications 1216 and/or radio 1218. Chipset1205 may provide intercommunication among processor 1210, memory 1212,storage 1214, graphics subsystem 1215, applications 1216 and/or radio1218. For example, chipset 1205 may include a storage adapter (notdepicted) capable of providing intercommunication with storage 1214.

Processor 1210 may be implemented as a Complex Instruction Set Computer(CISC) or Reduced Instruction Set Computer (RISC) processors, x86instruction set compatible processors, multi-core, or any othermicroprocessor or central processing unit (CPU). In variousimplementations, processor 1210 may be dual-core processor(s), dual-coremobile processor(s), and so forth.

Memory 1212 may be implemented as a volatile memory device such as, butnot limited to, a Random Access Memory (RAM), Dynamic Random AccessMemory (DRAM), or Static RAM (SRAM).

Storage 1214 may be implemented as a non-volatile storage device suchas, but not limited to, a magnetic disk drive, optical disk drive, tapedrive, an internal storage device, an attached storage device, flashmemory, battery backed-up SDRAM (synchronous DRAM), and/or a networkaccessible storage device. In various implementations, storage 1214 mayinclude technology to increase the storage performance enhancedprotection for valuable digital media when multiple hard drives areincluded, for example.

Graphics subsystem 1215 may perform processing of images such as stillor video for display. Graphics subsystem 1215 may be a graphicsprocessing unit (GPU) or a visual processing unit (VPU), for example. Ananalog or digital interface may be used to communicatively couplegraphics subsystem 1215 and display 1220. For example, the interface maybe any of a High-Definition Multimedia Interface, DisplayPort, wirelessHDMI, and/or wireless HD compliant techniques. Graphics subsystem 1215may be integrated into processor 1210 or chipset 1205. In someimplementations, graphics subsystem 1215 may be a stand-alone devicecommunicatively coupled to chipset 1205.

The graphics and/or video processing techniques described herein may beimplemented in various hardware architectures. For example, graphicsand/or video functionality may be integrated within a chipset.Alternatively, a discrete graphics and/or video processor may be used.As still another implementation, the graphics and/or video functions maybe provided by a general purpose processor, including a multi-coreprocessor. In further embodiments, the functions may be implemented in aconsumer electronics device.

Radio 1218 may include one or more radios capable of transmitting andreceiving signals using various suitable wireless communicationstechniques. Such techniques may involve communications across one ormore wireless networks. Example wireless networks include (but are notlimited to) wireless local area networks (WLANs), wireless personal areanetworks (WPANs), wireless metropolitan area network (WMANs), cellularnetworks, and satellite networks. In communicating across such networks,radio 1218 may operate in accordance with one or more applicablestandards in any version.

In various implementations, display 1220 may include any television typemonitor or display. Display 1220 may include, for example, a computerdisplay screen, touch screen display, video monitor, television-likedevice, and/or a television. Display 1220 may be digital and/or analog.In various implementations, display 1220 may be a holographic display.Also, display 1220 may be a transparent surface that may receive avisual projection. Such projections may convey various forms ofinformation, images, and/or objects. For example, such projections maybe a visual overlay for a mobile augmented reality (MAR) application.Under the control of one or more software applications 1216, platform1202 may display user interface 1222 on display 1220.

In various implementations, content services device(s) 1230 may behosted by any national, international and/or independent service andthus accessible to platform 1202 via the Internet, for example. Contentservices device(s) 1230 may be coupled to platform 1202 and/or todisplay 1220. Platform 1202 and/or content services device(s) 1230 maybe coupled to a network 1260 to communicate (e.g., send and/or receive)media information to and from network 1260. Content delivery device(s)1240 also may be coupled to platform 1202 and/or to display 1220.

In various implementations, content services device(s) 1230 may includea cable television box, personal computer, network, telephone, Internetenabled devices or appliance capable of delivering digital informationand/or content, and any other similar device capable ofuni-directionally or bi-directionally communicating content betweencontent providers and platform 1202 and/display 1220, via network 1260or directly. It will be appreciated that the content may be communicateduni-directionally and/or bi-directionally to and from any one of thecomponents in system 1200 and a content provider via network 1260.Examples of content may include any media information including, forexample, video, music, medical and gaming information, and so forth.

Content services device(s) 1230 may receive content such as cabletelevision programming including media information, digital information,and/or other content. Examples of content providers may include anycable or satellite television or radio or Internet content providers.The provided examples are not meant to limit implementations inaccordance with the present disclosure in any way.

In various implementations, platform 1202 may receive control signalsfrom navigation controller 1250 having one or more navigation features.The navigation features of navigation controller 1250 may be used tointeract with user interface 1222, for example. In various embodiments,navigation controller 1250 may be a pointing device that may be acomputer hardware component (specifically, a human interface device)that allows a user to input spatial (e.g., continuous andmulti-dimensional) data into a computer. Many systems such as graphicaluser interfaces (GUI), and televisions and monitors allow the user tocontrol and provide data to the computer or television using physicalgestures.

Movements of the navigation features of navigation controller 1250 maybe replicated on a display (e.g., display 1220) by movements of apointer, cursor, focus ring, or other visual indicators displayed on thedisplay. For example, under the control of software applications 1216,the navigation features located on navigation controller 1250 may bemapped to virtual navigation features displayed on user interface 1222,for example. In various embodiments, navigation controller 1250 may notbe a separate component but may be integrated into platform 1202 and/ordisplay 1220. The present disclosure, however, is not limited to theelements or in the context shown or described herein.

In various implementations, drivers (not shown) may include technologyto enable users to instantly turn on and off platform 1202 like atelevision with the touch of a button after initial boot-up, whenenabled, for example. Program logic may allow platform 1202 to streamcontent to media adaptors or other content services device(s) 1230 orcontent delivery device(s) 1240 even when the platform is turned “off.”In addition, chipset 1205 may include hardware and/or software supportfor 5.1 surround sound audio and/or high definition 7.1 surround soundaudio, for example. Drivers may include a graphics driver for integratedgraphics platforms. In various embodiments, the graphics driver mayinclude a peripheral component interconnect (PCI) Express graphics card.

In various implementations, any one or more of the components shown insystem 1200 may be integrated. For example, platform 1202 and contentservices device(s) 1230 may be integrated, or platform 1202 and contentdelivery device(s) 1240 may be integrated, or platform 1202, contentservices device(s) 1230, and content delivery device(s) 1240 may beintegrated, for example. In various embodiments, platform 1202 anddisplay 1220 may be an integrated unit. Display 1220 and content servicedevice(s) 1230 may be integrated, or display 1220 and content deliverydevice(s) 1240 may be integrated, for example. These examples are notmeant to limit the present disclosure.

In various embodiments, system 1200 may be implemented as a wirelesssystem, a wired system, or a combination of both. When implemented as awireless system, system 1200 may include components and interfacessuitable for communicating over a wireless shared media, such as one ormore antennas, transmitters, receivers, transceivers, amplifiers,filters, control logic, and so forth. An example of wireless sharedmedia may include portions of a wireless spectrum, such as the RFspectrum and so forth. When implemented as a wired system, system 1200may include components and interfaces suitable for communicating overwired communications media, such as input/output (I/O) adapters,physical connectors to connect the I/O adapter with a correspondingwired communications medium, a network interface card (NIC), disccontroller, video controller, audio controller, and the like. Examplesof wired communications media may include a wire, cable, metal leads,printed circuit board (PCB), backplane, switch fabric, semiconductormaterial, twisted-pair wire, co-axial cable, fiber optics, and so forth.

Platform 1202 may establish one or more logical or physical channels tocommunicate information. The information may include media informationand control information. Media information may refer to any datarepresenting content meant for a user. Examples of content may include,for example, data from a voice conversation, videoconference, streamingvideo, electronic mail (“email”) message, voice mail message,alphanumeric symbols, graphics, image, video, text and so forth. Datafrom a voice conversation may be, for example, speech information,silence periods, background noise, comfort noise, tones and so forth.Control information may refer to any data representing commands,instructions or control words meant for an automated system. Forexample, control information may be used to route media informationthrough a system, or instruct a node to process the media information ina predetermined manner. The embodiments, however, are not limited to theelements or in the context shown or described in FIG. 12.

As described above, system 1200 may be embodied in varying physicalstyles or form factors. FIG. 13 illustrates an example small form factordevice 1300, arranged in accordance with at least some implementationsof the present disclosure. In some examples, system 1200 may beimplemented via device 1300. In other examples, other systems discussedherein or portions thereof may be implemented via device 1300. Invarious embodiments, for example, device 1300 may be implemented as amobile computing device a having wireless capabilities. A mobilecomputing device may refer to any device having a processing system anda mobile power source or supply, such as one or more batteries, forexample.

Examples of a mobile computing device may include a personal computer(PC), laptop computer, ultra-laptop computer, tablet, touch pad,portable computer, handheld computer, palmtop computer, personal digitalassistant (PDA), cellular telephone, combination cellular telephone/PDA,smart device (e.g., smartphone, smart tablet or smart mobiletelevision), mobile internet device (MID), messaging device, datacommunication device, cameras (e.g. point-and-shoot cameras, super-zoomcameras, digital single-lens reflex (DSLR) cameras), and so forth.

Examples of a mobile computing device also may include computers thatare arranged to be worn by a person, such as a wrist computers, fingercomputers, ring computers, eyeglass computers, belt-clip computers,arm-band computers, shoe computers, clothing computers, and otherwearable computers. In various embodiments, for example, a mobilecomputing device may be implemented as a smartphone capable of executingcomputer applications, as well as voice communications and/or datacommunications. Although some embodiments may be described with a mobilecomputing device implemented as a smartphone by way of example, it maybe appreciated that other embodiments may be implemented using otherwireless mobile computing devices as well. The embodiments are notlimited in this context.

As shown in FIG. 13, device 1300 may include a housing with a front 1301and a back 1302. Device 1300 includes a display 1304, an input/output(I/O) device 1306, color camera 104, color camera 105, infraredtransmitter 204, and an integrated antenna 1308. Device 1300 also mayinclude navigation features 1312. I/O device 1306 may include anysuitable I/O device for entering information into a mobile computingdevice. Examples for I/O device 1306 may include an alphanumerickeyboard, a numeric keypad, a touch pad, input keys, buttons, switches,microphones, speakers, voice recognition device and software, and soforth. Information also may be entered into device 1300 by way ofmicrophone (not shown), or may be digitized by a voice recognitiondevice. As shown, device 1300 may include color cameras 104, 105 and aflash 1310 integrated into back 1302 (or elsewhere) of device 1300. Inother examples, color cameras 104, 105 and flash 1310 may be integratedinto front 1301 of device 1300 or both front and back sets of camerasmay be provided. Color cameras 104, 105 and a flash 1310 may becomponents of a camera module to originate color image data with IRtexture correction that may be processed into an image or streamingvideo that is output to display 1304 and/or communicated remotely fromdevice 1300 via antenna 1308 for example.

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as IP cores may be storedon a tangible, machine readable medium and supplied to various customersor manufacturing facilities to load into the fabrication machines thatactually make the logic or processor.

While certain features set forth herein have been described withreference to various implementations, this description is not intendedto be construed in a limiting sense. Hence, various modifications of theimplementations described herein, as well as other implementations,which are apparent to persons skilled in the art to which the presentdisclosure pertains are deemed to lie within the spirit and scope of thepresent disclosure.

In one or more first embodiments, an imaging device comprises aninfrared (IR) projector to project an IR texture pattern onto a scene,an image sensor to generate raw input image data based on an imagecapture of the scene comprising the projection of the IR texturepattern, such that the raw input image data comprises an IR texturepattern residual from the IR texture pattern, and an image signalprocessor coupled to the image sensor, the image signal processor toreceive the raw input image data and to apply a color correctiontransform to the raw input image data or image data corresponding to theraw input image data to generate output image data, such that the colorcorrection transform is to correct for the IR texture pattern residualsuch that the output image data has a reduced IR texture patternresidual with respect to the raw input image data.

In one or more second embodiments, further to the first embodiments, theimage signal processor is further to apply a second color correctiontransform to the raw input image data or the image data corresponding tothe raw input image data to generate IR texture output image data, suchthat the second color correction transform is to retain the IR texturepattern residual within the IR texture output image data and the outputimage data has a reduced IR texture pattern residual with respect to theIR texture output image data.

In one or more third embodiments, further to the first or secondembodiments, a plurality of pixel positions within the IR texture outputimage data comprise the IR texture pattern residual and such that theoutput image data having a reduced IR texture pattern residual withrespect to the IR texture output image data comprises the IR textureoutput image data having a first average luminance at the plurality ofpixel positions within the IR texture output image data and the outputimage data having a second average luminance at the same plurality ofpixel positions within the output image data that is less than the firstaverage luminance.

In one or more fourth embodiments, further to the first through thirdembodiments, the color correction transform is implemented in a firstimage processing pipeline of the image signal processor and the secondcolor correction transform is implemented in a second image processingpipeline of the image signal processor.

In one or more fifth embodiments, further to the first through fourthembodiments, the imaging device further comprises a second image sensorto generate second raw input image data based on a second image captureof the scene having the projection of the IR texture pattern, such thatthe second raw input image data comprises a second IR texture patternresidual from the IR texture pattern, and such that the first and secondimage sensors are horizontally aligned with respect to the scene

In one or more sixth embodiments, further to the first through fifthembodiments, the imaging device further comprises a processor coupled tothe image signal processor, the processor to generate a depth map basedon the IR texture output image data corresponding to the raw input imagedata and second IR texture image data corresponding to the second rawinput image data.

In one or more seventh embodiments, further to the first through sixthembodiments, the processor to generate the depth map comprises theprocessor to perform stereoscopic image matching based on the IR textureimage and the second IR texture image.

In one or more eighth embodiments, further to the first through seventhembodiments, the image signal processor is further to receive the secondraw input image data and to apply the color correction transform to thesecond raw input image data or image data corresponding to the secondraw input image data to generate second output image data.

In one or more ninth embodiments, further to the first through eighthembodiments, the image signal processor to apply the color correctiontransform comprises the image signal processor to apply the colorcorrection transform to a plurality of sub-pixel signals of the rawinput image data or image data corresponding to the raw input image datato generate a corresponding single pixel of the output image data, suchthat the sub-pixel signals comprises at least one red sub-pixel signalvalue, at least one green sub-pixel signal value, and at least one bluesub-pixel signal value.

In one or more tenth embodiments, further to the first through ninthembodiments, the plurality of sub-pixel signals consists of a single redsub-pixel signal value, two green sub-pixel signal values, and a singleblue sub-pixel signal value and the single pixel of the output imagedata comprises a red pixel value, a green pixel value, and a blue pixelvalue.

In one or more eleventh embodiments, further to the first through tenthembodiments, the image sensor comprises a complementarymetal-oxide-semiconductor (CMOS) sensor having a red-green-blue colorfilter array thereon and the image signal processor comprises anapplication-specific integrated circuit (ASIC).

In one or more twelfth embodiments, method for image processingcomprises receiving raw input image data comprising an infrared (IR)texture pattern residual from an IR texture pattern projected on a sceneduring an image capture of the scene, applying a color correctiontransform to the raw input image data or image data corresponding to theraw input image data to generate output image data, wherein the colorcorrection transform is to correct for the IR texture pattern residualsuch that the output image data has a reduced IR texture patternresidual with respect to the raw input image data, and providing theoutput image data for display to a user or for use in computer visionprocessing.

In one or more thirteenth embodiments, further to the twelfthembodiments, the method further comprises applying a second colorcorrection transform to the raw input image data or the image datacorresponding to the raw input image data to generate IR texture outputimage data, wherein the second color correction transform is to retainthe IR texture pattern residual within the IR texture output image dataand the output image data has a reduced IR texture pattern residual withrespect to the IR texture output image data.

In one or more fourteenth embodiments, further to the twelfth orthirteenth embodiments, a plurality of pixel positions within the IRtexture output image data comprise the IR texture pattern residual andwherein the output image data having a reduced IR texture patternresidual with respect to the IR texture output image data comprises theIR texture output image data having a first average luminance at theplurality of pixel positions within the IR texture output image data andthe output image data having a second average luminance at the sameplurality of pixel positions within the output image data that is lessthan the first average luminance.

In one or more fifteenth embodiments, further to the twelfth throughfourteenth embodiments, the method further comprises receiving secondraw input image data based on a second image capture of the scene havingthe projection of the IR texture pattern, wherein the second raw inputimage data comprises a second IR texture pattern residual from the IRtexture pattern, and wherein the raw input image data and the second rawinput image data are from first and second image sensors that arehorizontally aligned with respect to the scene.

In one or more sixteenth embodiments, further to the twelfth throughfifteenth embodiments, the method further comprises generating a depthmap based on a first IR texture image corresponding to the raw inputimage data and a second IR texture image corresponding to the second rawinput image data.

In one or more seventeenth embodiments, further to the twelfth throughsixteenth embodiments, generating the depth map comprises performingstereoscopic image matching based on the IR texture image and the secondIR texture image.

In one or more eighteenth embodiments, further to the twelfth throughseventeenth embodiments, applying the color correction transformcomprises applying the color correction transform to a plurality ofsub-pixel signals of the raw input image data or image datacorresponding to the raw input image data to generate a correspondingsingle pixel of the output image data, wherein the sub-pixel signalscomprises at least one red sub-pixel signal value, at least one greensub-pixel signal value, and at least one blue sub-pixel signal value.

In one or more nineteenth embodiments, further to the twelfth througheighteenth embodiments, the plurality of sub-pixel signals consists of asingle red sub-pixel signal value, two green sub-pixel signal values,and a single blue sub-pixel signal value and the single pixel of theoutput image data comprises a red pixel value, a green pixel value, anda blue pixel value.

In one or more twentieth embodiments, at least one machine readablemedium comprises a plurality of instructions that, in response to beingexecuted on a device, cause the device to perform image processing byreceiving raw input image data comprising an infrared (IR) texturepattern residual from an IR texture pattern projected on a scene duringan image capture of the scene, applying a color correction transform tothe raw input image data or image data corresponding to the raw inputimage data to generate output image data, wherein the color correctiontransform is to correct for the IR texture pattern residual such thatthe output image data has a reduced IR texture pattern residual withrespect to the raw input image data, and providing the output image datafor display to a user or for use in computer vision processing.

In one or more twenty-first embodiments, further to the twentiethembodiments, the machine readable medium comprises further instructionsthat, in response to being executed on the device, cause the device toperform image processing by applying a second color correction transformto the raw input image data or the image data corresponding to the rawinput image data to generate IR texture output image data, wherein thesecond color correction transform is to retain the IR texture patternresidual within the IR texture output image data and the output imagedata has a reduced IR texture pattern residual with respect to the IRtexture output image data.

In one or more twenty-second embodiments, further to the twentieth ortwenty-first embodiments, a plurality of pixel positions within the IRtexture output image data comprise the IR texture pattern residual andwherein the output image data having a reduced IR texture patternresidual with respect to the IR texture output image data comprises theIR texture output image data having a first average luminance at theplurality of pixel positions within the IR texture output image data andthe output image data having a second average luminance at the sameplurality of pixel positions within the output image data that is lessthan the first average luminance.

In one or more twenty-third embodiments, further to the twentieththrough twenty-second embodiments, the machine readable medium comprisesfurther instructions that, in response to being executed on the device,cause the device to perform image processing by receiving second rawinput image data based on a second image capture of the scene having theprojection of the IR texture pattern, wherein the second raw input imagedata comprises a second IR texture pattern residual from the IR texturepattern, and wherein the raw input image data and the second raw inputimage data are from first and second image sensors that are horizontallyaligned with respect to the scene.

In one or more twenty-fourth embodiments, further to the twentieththrough twenty-third embodiments, the machine readable medium comprisesfurther instructions that, in response to being executed on the device,cause the device to perform image processing by generating a depth mapbased on a first IR texture image corresponding to the raw input imagedata and a second IR texture image corresponding to the second raw inputimage data.

In one or more twenty-fifth embodiments, further to the twentieththrough twenty-fourth embodiments, applying the color correctiontransform comprises applying the color correction transform to aplurality of sub-pixel signals of the raw input image data or image datacorresponding to the raw input image data to generate a correspondingsingle pixel of the output image data, wherein the sub-pixel signalscomprises at least one red sub-pixel signal value, at least one greensub-pixel signal value, and at least one blue sub-pixel signal value.

In one or more twenty-sixth embodiments, an imaging device comprisesmeans for projecting an IR texture pattern onto a scene, means forgenerating raw input image data based on an image capture of the scenecomprising the projection of the IR texture pattern, wherein the rawinput image data comprises an IR texture pattern residual from the IRtexture pattern, and means for receiving the raw input image data andapplying a color correction transform to the raw input image data orimage data corresponding to the raw input image data to generate outputimage data, wherein the color correction transform is to correct for theIR texture pattern residual such that the output image data has areduced IR texture pattern residual with respect to the raw input imagedata.

In one or more twenty-seventh embodiments, further to the twenty-sixthembodiments, the imaging device further comprises means for applying asecond color correction transform to the raw input image data or theimage data corresponding to the raw input image data to generate IRtexture output image data, wherein the second color correction transformis to retain the IR texture pattern residual within the IR textureoutput image data and the output image data has a reduced IR texturepattern residual with respect to the IR texture output image data.

In one or more twenty-eighth embodiments, at least one machine readablemedium may include a plurality of instructions that in response to beingexecuted on a computing device, causes the computing device to perform amethod according to any one of the above embodiments.

In one or more twenty-ninth embodiments, an apparatus may include meansfor performing a method according to any one of the above embodiments.

It will be recognized that the embodiments are not limited to theembodiments so described, but can be practiced with modification andalteration without departing from the scope of the appended claims. Forexample, the above embodiments may include specific combination offeatures. However, the above embodiments are not limited in this regardand, in various implementations, the above embodiments may include theundertaking only a subset of such features, undertaking a differentorder of such features, undertaking a different combination of suchfeatures, and/or undertaking additional features than those featuresexplicitly listed. The scope of the embodiments should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. An imaging device comprising: an infrared (IR)projector to project an IR texture pattern onto a scene; an image sensorto generate raw input image data based on an image capture of the scenecomprising the projection of the IR texture pattern, wherein the rawinput image data comprises an IR texture pattern residual from the IRtexture pattern; and an image signal processor coupled to the imagesensor, the image signal processor to receive the raw input image dataand to apply a color correction transform to the raw input image data orimage data corresponding to the raw input image data to generate outputimage data, wherein the color correction transform is to correct for theIR texture pattern residual such that the output image data has areduced IR texture pattern residual with respect to the raw input imagedata.
 2. The imaging device of claim 1, wherein the image signalprocessor is further to apply a second color correction transform to theraw input image data or the image data corresponding to the raw inputimage data to generate IR texture output image data, wherein the secondcolor correction transform is to retain the IR texture pattern residualwithin the IR texture output image data and the output image data has areduced IR texture pattern residual with respect to the IR textureoutput image data.
 3. The imaging device of claim 2, wherein a pluralityof pixel positions within the IR texture output image data comprise theIR texture pattern residual and wherein the output image data having areduced IR texture pattern residual with respect to the IR textureoutput image data comprises the IR texture output image data having afirst average luminance at the plurality of pixel positions within theIR texture output image data and the output image data having a secondaverage luminance at the same plurality of pixel positions within theoutput image data that is less than the first average luminance.
 4. Theimaging device of claim 2, wherein the color correction transform isimplemented in a first image processing pipeline of the image signalprocessor and the second color correction transform is implemented in asecond image processing pipeline of the image signal processor.
 5. Theimaging device of claim 2, further comprising: a second image sensor togenerate second raw input image data based on a second image capture ofthe scene having the projection of the IR texture pattern, wherein thesecond raw input image data comprises a second IR texture patternresidual from the IR texture pattern, and wherein the first and secondimage sensors are horizontally aligned with respect to the scene.
 6. Theimaging device of claim 5, further comprising: a processor coupled tothe image signal processor, the processor to generate a depth map basedon the IR texture output image data corresponding to the raw input imagedata and second IR texture image data corresponding to the second rawinput image data.
 7. The imaging device of claim 6, wherein theprocessor to generate the depth map comprises the processor to performstereoscopic image matching based on the IR texture image and the secondIR texture image.
 8. The imaging device of claim 5, wherein the imagesignal processor is further to receive the second raw input image dataand to apply the color correction transform to the second raw inputimage data or image data corresponding to the second raw input imagedata to generate second output image data.
 9. The imaging device ofclaim 1, wherein the image signal processor to apply the colorcorrection transform comprises the image signal processor to apply thecolor correction transform to a plurality of sub-pixel signals of theraw input image data or image data corresponding to the raw input imagedata to generate a corresponding single pixel of the output image data,wherein the sub-pixel signals comprises at least one red sub-pixelsignal value, at least one green sub-pixel signal value, and at leastone blue sub-pixel signal value.
 10. The imaging device of claim 9,wherein the plurality of sub-pixel signals consists of a single redsub-pixel signal value, two green sub-pixel signal values, and a singleblue sub-pixel signal value and the single pixel of the output imagedata comprises a red pixel value, a green pixel value, and a blue pixelvalue.
 11. The imaging device of claim 1, wherein the image sensorcomprises a complementary metal-oxide-semiconductor (CMOS) sensor havinga red-green-blue color filter array thereon and the image signalprocessor comprises an application-specific integrated circuit (ASIC).12. A method for image processing comprising: receiving raw input imagedata comprising an infrared (IR) texture pattern residual from an IRtexture pattern projected on a scene during an image capture of thescene; applying a color correction transform to the raw input image dataor image data corresponding to the raw input image data to generateoutput image data, wherein the color correction transform is to correctfor the IR texture pattern residual such that the output image data hasa reduced IR texture pattern residual with respect to the raw inputimage data; and providing the output image data for display to a user orfor use in computer vision processing.
 13. The method of claim 12,further comprising: applying a second color correction transform to theraw input image data or the image data corresponding to the raw inputimage data to generate IR texture output image data, wherein the secondcolor correction transform is to retain the IR texture pattern residualwithin the IR texture output image data and the output image data has areduced IR texture pattern residual with respect to the IR textureoutput image data.
 14. The method of claim 13, wherein a plurality ofpixel positions within the IR texture output image data comprise the IRtexture pattern residual and wherein the output image data having areduced IR texture pattern residual with respect to the IR textureoutput image data comprises the IR texture output image data having afirst average luminance at the plurality of pixel positions within theIR texture output image data and the output image data having a secondaverage luminance at the same plurality of pixel positions within theoutput image data that is less than the first average luminance.
 15. Themethod of claim 14, further comprising: receiving second raw input imagedata based on a second image capture of the scene having the projectionof the IR texture pattern, wherein the second raw input image datacomprises a second IR texture pattern residual from the IR texturepattern, and wherein the raw input image data and the second raw inputimage data are from first and second image sensors that are horizontallyaligned with respect to the scene.
 16. The method of claim 15, furthercomprising: generating a depth map based on a first IR texture imagecorresponding to the raw input image data and a second IR texture imagecorresponding to the second raw input image data.
 17. The method ofclaim 12, wherein applying the color correction transform comprisesapplying the color correction transform to a plurality of sub-pixelsignals of the raw input image data or image data corresponding to theraw input image data to generate a corresponding single pixel of theoutput image data, wherein the sub-pixel signals comprises at least onered sub-pixel signal value, at least one green sub-pixel signal value,and at least one blue sub-pixel signal value.
 18. At least one machinereadable medium comprising a plurality of instructions that, in responseto being executed on a device, cause the device to perform imageprocessing by: receiving raw input image data comprising an infrared(IR) texture pattern residual from an IR texture pattern projected on ascene during an image capture of the scene; applying a color correctiontransform to the raw input image data or image data corresponding to theraw input image data to generate output image data, wherein the colorcorrection transform is to correct for the IR texture pattern residualsuch that the output image data has a reduced IR texture patternresidual with respect to the raw input image data; and providing theoutput image data for display to a user or for use in computer visionprocessing.
 19. The machine readable medium of claim 18, the machinereadable medium comprising further instructions that, in response tobeing executed on the device, cause the device to perform imageprocessing by: applying a second color correction transform to the rawinput image data or the image data corresponding to the raw input imagedata to generate IR texture output image data, wherein the second colorcorrection transform is to retain the IR texture pattern residual withinthe IR texture output image data and the output image data has a reducedIR texture pattern residual with respect to the IR texture output imagedata.
 20. The machine readable medium of claim 19, wherein a pluralityof pixel positions within the IR texture output image data comprise theIR texture pattern residual and wherein the output image data having areduced IR texture pattern residual with respect to the IR textureoutput image data comprises the IR texture output image data having afirst average luminance at the plurality of pixel positions within theIR texture output image data and the output image data having a secondaverage luminance at the same plurality of pixel positions within theoutput image data that is less than the first average luminance.
 21. Themachine readable medium of claim 19, the machine readable mediumcomprising further instructions that, in response to being executed onthe device, cause the device to perform image processing by: receivingsecond raw input image data based on a second image capture of the scenehaving the projection of the IR texture pattern, wherein the second rawinput image data comprises a second IR texture pattern residual from theIR texture pattern, and wherein the raw input image data and the secondraw input image data are from first and second image sensors that arehorizontally aligned with respect to the scene.
 22. The machine readablemedium of claim 21, the machine readable medium comprising furtherinstructions that, in response to being executed on the device, causethe device to perform image processing by: generating a depth map basedon a first IR texture image corresponding to the raw input image dataand a second IR texture image corresponding to the second raw inputimage data.
 23. The machine readable medium of claim 18, whereinapplying the color correction transform comprises applying the colorcorrection transform to a plurality of sub-pixel signals of the rawinput image data or image data corresponding to the raw input image datato generate a corresponding single pixel of the output image data,wherein the sub-pixel signals comprises at least one red sub-pixelsignal value, at least one green sub-pixel signal value, and at leastone blue sub-pixel signal value.